Social environment-based opportunity costs dictate when people leave social interactions

There is an ever-increasing understanding of the cognitive mechanisms underlying how we process others’ behaviours during social interactions. However, little is known about how people decide when to leave an interaction. Are these decisions shaped by alternatives in the environment – the opportunity-costs of connecting to other people? Here, participants chose when to leave partners who treated them with varying degrees of fairness, and connect to others, in social environments with different opportunity-costs. Across four studies we find people leave partners more quickly when opportunity-costs are high, both the average fairness of people in the environment and the effort required to connect to another partner. People’s leaving times were accounted for by a fairness-adapted evidence accumulation model, and modulated by depression and loneliness scores. These findings demonstrate the computational processes underlying decisions to leave, and highlight atypical social time allocations as a marker of poor mental health.


Sample size justification
We ran the statistical model on 5000 simulated datasets based on the fixed effects and variance of the random effects from the analysis of Study 1. Power for each fixed effect was calculated as the proportion of simulations where p < 0.05.This analysis suggested data from 25 participants would give us over 90% power to detect main effects of partner and environment type, and over 80% power to detect an interaction, with alpha at 0.05.Therefore, we set this as our target sample size for Studies 2 and 3. We were uncertain as to a likely effect size for the exploratory analyses examining the role of depressive symptoms in Study 4, so aimed to recruit 100 participants for this study.It is recommended that post-hoc power analyses are not used as an estimate of power (Dziak et al., 2020).However, a recommended alternative is to calculate the bootstrapped confidence interval of the beta value and if 0 is not within the interval it provides evidence that the study has sufficient power.We recognise that sufficient power for three-way interactions is difficult to achieve, so this procedure has been carried out on the model parameters from the depression analysis of Study 4 (Supplementary Figure 2).

Control analysis (Study 1 & 2) -excluding zero fairness trials
The fairness trajectories used in Study 1 and Study 2 (see methods for details) allowed for fairness to drop to zero.Continuing to interact with a partner while they share no money is a legitimate behaviour in the task, but it could also be a sign of inattention.We repeated the analyses from both study 1 and study 2, excluding decisions made after the fairness had dropped to zero.The main effects of partner and environment type remained statistically significant for both studies, while the interaction between predictors was non-significant for both studies (Supplementary Table 1).

Supplementary analysis -fairness at leaving
We examined whether the fairness at the time of choosing to leave a partner differed by partner type or social environment for each study.Across all studies there was a main effect of both partner type and social environment, and no interaction.In all cases, the fairness at leaving was lower for less fair partners than fairer partners, and in low quality environments compared to high.

Supplementary analysis -earnings
We examined whether the amount earned per partner differed by partner type or social environment for each study.Across all studies there was a main effect of both partner type and social environment.In all cases, participants earned less with unfair partners than fairer partners, and earned more in low quality environments than high quality environments.Since participants accumulate reward while interacting with a partner, and earn no reward while travelling between partners, this pattern of results is to be expected.Leaving times were earlier in a high opportunity cost environment, meaning more time was spent travelling, and therefore less reward earned.
People spent more time interacting with fair partners than unfair partners, and therefore earned more money with those partners.There was a partner by environment interaction in Study 1, but this did not replicate across studies.To test whether the altered behaviour seen in participants with higher self-reported depression and loneliness was more economically rational, we carried out two regression analyses predicting overall earnings by depression or loneliness.Supplementary Table 5 shows that neither of these predictors were significant.

Supplementary Figure 2 :
Histogram of 1000 bootstrapped beta values for the three-way interaction in the depression (A) and loneliness (B) analyses of Study 4. Vertical lines indicate the bootstrapped 95% confidence intervals.The exclusion of zero from these intervals suggests sufficient power to detect this effect.

Table 3 :
Results from linear mixed models for all studies, with fairness as the outcome variable Supplementary Table4: Results from linear mixed models for all studies, with earnings as the outcome variable